The increasing use of statistical data analysis in enterprise applications
has created an arms race among database vendors to offer ever more
sophisticated in-database analytics. One challenge in this race is that each
new statistical technique must be implemented from scratch in the RDBMS, which
leads to a lengthy and complex development process. We argue that the root
cause for this overhead is the lack of a unified architecture for in-database
analytics. Our main contribution in this work is to take a step towards such a
unified architecture. A key benefit of our unified architecture is that
performance optimizations for analytics techniques can be studied generically
instead of an ad hoc, per-technique fashion. In particular, our technical
contributions are theoretical and empirical studies of two key factors that we
found impact performance: the order data is stored, and parallelization of
computations on a single-node multicore RDBMS. We demonstrate the feasibility
of our architecture by integrating several popular analytics techniques into
two commercial and one open-source RDBMS. Our architecture requires changes to
only a few dozen lines of code to integrate a new statistical technique. We
then compare our approach with the native analytics tools offered by the
commercial RDBMSes on various analytics tasks, and validate that our approach
achieves competitive or higher performance, while still achieving the same
quality.

本文针对数据库供应商在提供日益复杂的 in-database analytics 功能时面临的开发难题进行探讨，提出了一种统一的架构来优化表现，其中包括对数据存储顺序和计算并行化的研究，并成功地将多种流行的分析技术集成到商业和开源 RDBMS 中，证明其具有与商业 RDBMS 本地工具相当或更高的性能。